Moudule 12 Course Data

---

import json
import pandas as pd
import plotly as plotly
import plotly.graph_objects as go
import plotly.express as px
from pandas import json_normalize

with open('Sample LRS rinnuja.json') as json_file:
    xapiData = json.load(json_file)

#https://www.delftstack.com/howto/python-pandas/json-to-pandas-dataframe/
df = json_normalize(xapiData)

# below feilds are all the same, english version of other feilds, or too general to be usefull right now. These feilds can be edited in revisons to xAPI
# del trimmed_df['id'] actually keep this, we can use this with the ip CSV
del df['object.id']
del df['object.objectType']
del df['authority.objectType']
del df['authority.account.homePage']
del df['authority.account.name']
del df['verb.id']
del df['stored']
del df['actor.mbox']
del df['object.definition.type']
del df['result.score.min']
# trimmed_df.reset_index(drop=True, inplace=True)

# lets deal wiht module 12 data

temp = df.loc[df['object.definition.name.en-US'] == "Week 5 Module 12: Friction"].copy()
for x in temp.index:
    if 'H' in temp.at[x, 'result.duration']:
       # print(x)
        time_string = temp.at [x, 'result.duration']
        #print(time_string)
        temp.at[x, 'result.duration'] = pd.to_datetime(temp.at[x, 'result.duration'], format='PT%HH%MM%SS').time()
       # print(temp.at[x, 'result.duration'] )
    elif 'M' in temp.at[x, 'result.duration']:
       # print(x)
        time_string = temp.at [x, 'result.duration']
        #print(time_string)
        temp.at[x, 'result.duration'] = pd.to_datetime(temp.at[x, 'result.duration'], format='PT%MM%SS').time()
        #print(temp.at[x, 'result.duration'] )
    else:
        #print(x)
        time_string = temp.at [x, 'result.duration']
       # print(time_string)
        temp.at[x, 'result.duration'] = pd.to_datetime(temp.at[x, 'result.duration'], format='PT%SS').time()
        #print(temp.at[x, 'result.duration'] )
df = temp.copy()

# create time delta from datetime.time objects 
df['result.duration.seconds'] = 'NaN'

temp = df.copy()
for x in temp.index:
    #print(x)
    time_string = temp.at [x, 'result.duration']
   # print(time_string)
    temp.at[x, 'result.duration.seconds'] = pd.to_timedelta(time_string.strftime( format="%H:%M:%S")).total_seconds()
    #print(temp.at[x, 'result.duration.seconds'] )
df = temp.copy()

# Dataframe.col_name.nunique()

fig = go.Figure()

fig.add_trace(go.Indicator(
    mode = 'number',
    value = df['actor.name'].nunique(),
    title = {'text': "unique actors"},
     domain = {'row': 0, 'column': 0}
    ))
# 
temp = df.loc[df['verb.display.en-US'] == "exited"].copy()
#

fig.add_trace(go.Indicator(
    mode = 'number',
    number = {'suffix': ' mins'},
    value = (temp['result.duration.seconds'].mean() / 60) ,
    title = {'text': "Average Module Completion"},
    domain = {'row': 0, 'column': 1}
    ))
# create grid    
fig.update_layout(
    grid = {'rows': 2, 'columns': 2})

#
fig.show()

# create list of answered descriptions
temp = df.loc[(df['verb.display.en-US'] == "answered") ].copy()
temp_desc = temp['object.definition.description.en-US'].unique()
# array has been sorted

quiz_num = 0
print(temp_desc.size)
question_avgs =  pd.DataFrame(columns = ['question', 'desc', 'seconds','raw score', 'scaled score'])

import math  

# make all the graphs at once
for x in range(0, temp_desc.size):
    if "3." in temp_desc[x]:
        quiz_num = quiz_num + 1
        print(temp_desc[x])
        finalquiz_df = temp.loc[(temp['object.definition.description.en-US'] == temp_desc[x])].copy()
        #finalquiz_df.head()
        # sort by
        finalquiz_df = finalquiz_df.sort_values(by=['result.response'])
        # create graphs
        fig = px.histogram(finalquiz_df, histfunc="count", x='result.response', color='result.response')
        fig.update_layout( xaxis={'categoryorder':'total descending'})
        fig.update_layout(title="Responses for Final Q" + str(quiz_num))
        fig.show()
        fig = px.scatter(finalquiz_df,x='actor.name', y = 'result.score.scaled', color='actor.name')
        fig.update_layout(title="Student Scores for Final Q"  + str(quiz_num))
        fig.show()
       
        fig = go.Figure()
        question_avgs= question_avgs.append({'question': quiz_num, 'desc': temp_desc[x] , 'seconds': finalquiz_df['result.duration.seconds'].mean(), 
                'raw score' :  math.ceil(finalquiz_df['result.score.raw'].mean() ) , 'scaled score' :  finalquiz_df['result.score.scaled'].mean() },ignore_index=True )
        ###
        fig = go.Figure()

        fig.add_trace(go.Indicator(
            mode = 'number',
            number = {'suffix': ' points'},
            value = ( (question_avgs.at[quiz_num - 1, 'raw score'] ) ),
            title = {'text': "Average Raw Score: Q" +  str(quiz_num) },
             domain = {'x': [0, 0.5], 'y': [0, 0.5]}
            ))

        fig.add_trace(go.Indicator(
            mode = 'number',
            number = {'suffix': ' points'},
            value = ( question_avgs.at[quiz_num - 1, 'scaled score'] ) ,
            title = {'text':"Average scaled Score: Q" +  str(quiz_num)},
            domain = {'x': [0, 0.5], 'y': [0.5, 1]}
            ))

        fig.add_trace(go.Indicator(
        mode = 'number', number = {'suffix': ' mins'},
        value = finalquiz_df['result.duration.seconds'].mean() / 60,
        title = {'text': "Average Time: Q"  +  str(quiz_num)},
        domain={'x': [0.6, 1], 'y': [0, 1]}
        ))
        # create grid    
        fig.update_layout(
            grid = {'rows': 3, 'columns': 2})

        fig.show()
        

     #   fig.add_trace(go.Indicator(
    #        mode = 'number', number = {'suffix': ' mins'},
   #         value = finalquiz_df['result.duration.seconds'].mean() / 60,
  #          title = {'text': "Average Time"}
 #           ))
#        fig.show()

10
Student has answered slide 3.2

Student has answered slide 3.3

Student has answered slide 3.4

Student has answered slide 3.5

Student has answered slide 3.6

Student has answered slide 3.7

fig = go.Figure()

fig.add_trace(go.Indicator(
    mode = 'number',
    number = {'suffix': ' mins'},
    value = ( question_avgs['seconds'].mean() / 60),
    title = {'text': "Average Question Response"},
    domain = {'row': 0, 'column': 0}
    ))

fig.add_trace(go.Indicator(
    mode = 'number',
    number = {'suffix': ' mins'},
    value = (( question_avgs['seconds'].sum() ) / 60) ,
    title = {'text': "Average Quiz Completion"},
    domain = {'row': 0, 'column': 1}
    ))
# create grid    
fig.update_layout(
    grid = {'rows': 2, 'columns': 2})

#
fig.show()

fig = px.line(question_avgs, x = 'question' , y = 'seconds', markers=True, title="Seconds Per Question")
fig.show()
fig = px.line(question_avgs, x = 'question' , y = 'raw score',  title="Raw Score Per Question", markers=True)
fig.show()
fig = px.line(question_avgs, x = 'question' , y = 'scaled score', title="scaled Score Per Question", markers=True)
fig.show()

#run this below to export as html and keep graph interactivity
plotly.offline.init_notebook_mode()